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Ovarian granulosa cells are essential to gonadotrophin-regulated estrogen production, female cycle maintenance and fertility. The epithelial Na+ channel (ENaC) is associated with female fertility; however, whether and how it plays a role in ovarian cell function(s) remained unexplored. Here, we report patch-clamp and Na+ imaging detection of ENaC expression and channel activity in both human and mouse ovarian granulosa cells, which are promoted by pituitary gonadotrophins, follicle stimulating hormone (FSH) or luteinizing hormone (LH). Cre-recombinase- and CRISPR-Cas9-based granulosa-specific knockout of ENaC α subunit (Scnn1a) in mice resulted in failed estrogen elevation at early estrus, reduced number of corpus luteum, abnormally extended estrus phase, reduced litter size and subfertility in adult female mice. Further analysis using technologies including RNA sequencing and Ca2+ imaging revealed that pharmacological inhibition, shRNA-based knockdown or the knockout of ENaC diminished spontaneous or stimulated Ca2+ oscillations, lowered the capacity of intracellular Ca2+ stores and impaired FSH/LH-stimulated transcriptome changes for estrogen production in mouse and/or human granulosa cells. Together, these results have revealed a previously undefined role of ENaC in modulating gonadotrophin signaling in granulosa cells for estrogen homeostasis and thus female fertility. [ABSTRACT FROM AUTHOR]

The multimode resonant cavity is the most common cavity. The material often shows on selective heating performance during the heating process due to the effect of microwave heating having a closely relationship with the electromagnetism parameters. This paper is based on finite difference time domain method (FDTD) to establish the electromagnetic-thermal model. The electromagnetic sensitivity property parameters of sodium chloride including relative dielectric constant, loss angle tangent and water content of sodium chloride is studied during the heating and drying process. The heating rate and the electric field distribution of sodium chloride, at the different water content, were simulated with the electromagnetic characteristic parameters changing. The results show that with the electromagnetic sensitivity property parameters varying, the electric field strength, heating rate and steady-state temperature of the heating material will all have a variety in the cavity. Some measures are proposed to improve the heating efficiency and ensure the stability of the microwave heating system in the industrial application.

Given the advancements in solar power generation and fifth‐generation (5G) technologies, it is crucial to reduce energy consumption based on accurate predictions of the photovoltaic power requirements of 5G base stations (BSs) connected to renewable energy sources. To ensure sufficient prediction performance even under complex weather conditions and promote two‐way flow of energy and information in the power supply system of the 5G BS, this work first proposes a reference scenario for a 5G BS with an energy router. Then, the comprehensive grey relational analysis is used to determine the optimal photovoltaic input variables. Finally, the improved logistic distribution, Laplacian distribution with inverse incomplete gamma‐function weight factor, and nonlinear mutation perturbation strategy are introduced to improve the ability of the sparrow search algorithm to avoid locally optimal solutions for an extreme learning machine of the short‐term photovoltaic power prediction model. The proposed model is applied to actual measured data for validation, and the results show that this model has the lowest mean percentage error as well as highest prediction accuracy under different weather conditions. The coefficient of determination for the proposed prediction model is greater than 99%; therefore, it is expected to improve the photovoltaic prediction performances of 5G energy‐routing BSs as well as promote sustainable development and sharing of energy and information. [ABSTRACT FROM AUTHOR]